We base our approach on a large body of recent work on predictive processing and active inference. The underlying assumption is that action recognition rests on principles of predictive processing [1]} where predictions about sensory stimuli are continuously formed and evaluated against incoming sensory input. An agent's systemic goal is to minimize so-called free energy, i.e., its uncertainty in terms of its surprise and divergence with respect to its predictions [2]}, [3]}. For action control, predictive processing views action as a form of active inference by which the environment is affected to reduce uncertainty about predictions that stem from beliefs about the world [4]}. The generative process is inverted to predict next actions and, thus, attenuate prediction errors [5]}. This mechanism has been discussed as a form of so-called affordance competition, a possible mechanism for action selection [6]} based on possible goals achievable through that action.
